Refine
Year of publication
- 2003 (9) (remove)
Document Type
- Article (9) (remove)
Language
- English (9)
Has Fulltext
- yes (9)
Is part of the Bibliography
- no (9)
Keywords
Institute
- Geowissenschaften (9) (remove)
Subvisible cirrus clouds (SVCs) may contribute to dehydration close to the tropical tropopause. The higher and colder SVCs and the larger their ice crystals, the more likely they represent the last efficient point of contact of the gas phase with the ice phase and, hence, the last dehydrating step, before the air enters the stratosphere. The first simultaneous in situ and remote sensing measurements of SVCs were taken during the APE-THESEO campaign in the western Indian ocean in February/March 1999. The observed clouds, termed Ultrathin Tropical Tropopause Clouds (UTTCs), belong to the geometrically and optically thinnest large-scale clouds in the Earth's atmosphere. Individual UTTCs may exist for many hours as an only 200–300 m thick cloud layer just a few hundred meters below the tropical cold point tropopause, covering up to 105 km2. With temperatures as low as 181 K these clouds are prime representatives for defining the water mixing ratio of air entering the lower stratosphere.
Mechanisms by which subvisible cirrus clouds (SVCs) might contribute to dehydration close to the tropical tropopause are not well understood. Recently Ultrathin Tropical Tropopause Clouds (UTTCs) with optical depths around 10−4 have been detected in the western Indian ocean. These clouds cover thousands of square kilometers as 200–300 m thick distinct and homogeneous layer just below the tropical tropopause. In their condensed phase UTTCs contain only 1–5% of the total water, and essentially no nitric acid. A new cloud stabilization mechanism is required to explain this small fraction of the condensed water content in the clouds and their small vertical thickness. This work suggests a mechanism, which forces the particles into a thin layer, based on upwelling of the air of some mm/s to balance the ice particles, supersaturation with respect to ice above and subsaturation below the UTTC. In situ measurements suggest that these requirements are fulfilled. The basic physical properties of this mechanism are explored by means of a single particle model. Comprehensive 1-D cloud simulations demonstrate this stabilization mechanism to be robust against rapid temperature fluctuations of +/−0.5 K. However, rapid warming (ΔT>2 K) leads to evaporation of the UTTC, while rapid cooling (ΔT<−2 K) leads to destabilization of the particles with the potential for significant dehydration below the cloud.
Mechanisms by which subvisible cirrus clouds (SVCs) might contribute to dehydration close to the tropical tropopause are not well understood. Recently Ultrathin Tropical Tropopause Clouds (UTTCs) with optical depths around 10-4 have been detected in the western Indian ocean. These clouds cover thousands of square kilometers as 200-300 m thick distinct and homogeneous layer just below the tropical tropopause. In their condensed phase UTTCs contain only 1-5% of the total water, and essentially no nitric acid. A new cloud stabilization mechanism is required to explain this small fraction of the condensed water content in the clouds and their small vertical thickness. This work suggests a mechanism, which forces the particles into a thin layer, based on upwelling of the air of some mm/s to balance the ice particles, supersaturation with respect to ice above and subsaturation below the UTTC. In situ measurements suggest that these requirements are fulfilled. The basic physical properties of this mechanism are explored by means of a single particle model. Comprehensive 1-D cloud simulations demonstrate this stabilization mechanism to be robust against rapid temperature fluctuations of +/- 0.5 K. However, rapid warming (Delta T > 2 K) leads to evaporation of the UTTC, while rapid cooling (Delta T < -2 K) leads to destabilization of the particles with the potential for significant dehydration below the cloud
Measurements of OH, total peroxy radicals, non-methane hydrocarbons (NMHCs) and various other trace gases were made at the Meteorological Observatory Hohenpeissenberg in June 2000. The data from an intensive measurement period characterised by high solar insolation (18-21 June) are analysed. The maximum midday OH concentration ranged between 4.5x106 molecules cm-3 and 7.4x106 molecules cm-3. The maximum total ROx (ROx =OH+RO+HO2+RO2) mixing ratio increased from about 55 pptv on 18 June to nearly 70 pptv on 20 and 21 June. A total of 64 NMHCs, including isoprene and monoterpenes, were measured every 1 to 6 hours. The oxidation rate of the NMHCs by OH was calculated and reached a total of over 14x106 molecules cm-3 s-1 on two days. A simple photostationary state balance model was used to simulate the ambient OH and peroxy radical concentrations with the measured data as input. This approach was able to reproduce the main features of the diurnal profiles of both OH and peroxy radicals. The balance equations were used to test the effect of the assumptions made in this model. The results proved to be most sensitive to assumptions about the impact of unmeasured volatile organic compounds (VOC), e.g. formaldehyde (HCHO), and about the partitioning between HO2 and RO2. The measured OH concentration and peroxy radical mixing ratios were reproduced well by assuming the presence of 3 ppbv HCHO as a proxy for oxygenated hydrocarbons, and a HO2/ RO2 ratio between 1:1 and 1:2. The most important source of OH, and conversely the greatest sink for peroxy radicals, was the recycling of HO2 radicals to OH. This reaction was responsible for the recycling of more than 45x106 molecules cm-3 s-1 on two days. The most important sink for OH, and the largest source of peroxy radicals, was the oxidation of NMHCs, in particular, of isoprene and the monoterpenes.
Subvisible cirrus clouds (SVCs) may contribute to dehydration close to the tropical tropopause. The higher and colder SVCs and the larger their ice crystals, the more likely they represent the last efficient point of contact of the gas phase with the ice phase and, hence, the last dehydrating step, before the air enters the stratosphere. The first simultaneous in situ and remote sensing measurements of SVCs were taken during the APE-THESEO campaign in the western Indian ocean in February/March 1999. The observed clouds, termed Ultrathin Tropical Tropopause Clouds (UTTCs), belong to the geometrically and optically thinnest large-scale clouds in the Earth´s atmosphere. Individual UTTCs may exist for many hours as an only 200--300 m thick cloud layer just a few hundred meters below the tropical cold point tropopause, covering up to 105 km2. With temperatures as low as 181 K these clouds are prime representatives for defining the water mixing ratio of air entering the lower stratosphere.
We report measurements of the deuterium content of molecular hydrogen (H2) obtained from a suite of air samples that were collected during a stratospheric balloon flight between 12 and 33 km at 40º N in October 2002. Strong deuterium enrichments of up to 400 permil versus Vienna Standard Mean Ocean Water (VSMOW) are observed, while the H2 mixing ratio remains virtually constant. Thus, as hydrogen is processed through the H2 reservoir in the stratosphere, deuterium is accumulated in H2 . Using box model calculations we investigated the effects of H2 sources and sinks on the stratospheric enrichments. Results show that considerable isotope enrichments in the production of H2 from CH4 must take place, i.e., deuterium is transferred preferentially to H2 during the CH4 oxidation sequence. This supports recent conclusions from tropospheric H2 isotope measurements which show that H2 produced photochemically from CH4 and non-methane hydrocarbons must be enriched in deuterium to balance the tropospheric hydrogen isotope budget. In the absence of further data on isotope fractionations in the individual reaction steps of the CH4 oxidation sequence, this effect cannot be investigated further at present. Our measurements imply that molecular hydrogen has to be taken into account when the hydrogen isotope budget in the stratosphere is investigated.
Temporal changes in the occurrence of extreme events in time series of observed precipitation are investigated. The analysis is based on a European gridded data set and a German station-based data set of recent monthly totals (1896=1899–1995=1998). Two approaches are used. First, values above certain defined thresholds are counted for the first and second halves of the observation period. In the second step time series components, such as trends, are removed to obtain a deeper insight into the causes of the observed changes. As an example, this technique is applied to the time series of the German station Eppenrod. It arises that most of the events concern extreme wet months whose frequency has significantly increased in winter. Whereas on the European scale the other seasons also show this increase, especially in autumn, in Germany an insignificant decrease in the summer and autumn seasons is found. Moreover it is demonstrated that the increase of extreme wet months is reflected in a systematic increase in the variance and the Weibull probability density function parameters, respectively.
The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Observed global and European spatiotemporal related fields of surface air temperature, mean-sea-level pressure and precipitation are analyzed statistically with respect to their response to external forcing factors such as anthropogenic greenhouse gases, anthropogenic sulfate aerosol, solar variations and explosive volcanism, and known internal climate mechanisms such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). As a first step, a principal component analysis (PCA) is applied to the observed spatiotemporal related fields to obtain spatial patterns with linear independent temporal structure. In a second step, the time series of each of the spatial patterns is subject to a stepwise regression analysis in order to separate it into signals of the external forcing factors and internal climate mechanisms as listed above as well as the residuals. Finally a back-transformation leads to the spatiotemporally related patterns of all these signals being intercompared. Two kinds of significance tests are applied to the anthropogenic signals. First, it is tested whether the anthropogenic signal is significant compared with the complete residual variance including natural variability. This test answers the question whether a significant anthropogenic climate change is visible in the observed data. As a second test the anthropogenic signal is tested with respect to the climate noise component only. This test answers the question whether the anthropogenic signal is significant among others in the observed data. Using both tests, regions can be specified where the anthropogenic influence is visible (second test) and regions where the anthropogenic influence has already significantly changed climate (first test).